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Neurology. 2012 May 1;78(18):1376-82. doi: 10.1212/WNL.0b013e318253d5b3. Epub 2012 Apr 4.

Predicting missing biomarker data in a longitudinal study of Alzheimer disease.

Collaborators (198)

Aisen P, Jack CR, Toga AW, Beckett L, Gamst A, Soares H, Green RC, Montine T, Thomas RG, Donohue M, Walter S, Dale A, Bernstein M, Felmlee J, Fox N, Thompson P, Schuff N, Alexander G, DeCarli C, Bandy D, Chen K, Morris J, Lee VM, Korecka M, Crawford K, Neu S, Harvey D, Kornak J, Saykin AJ, Foroud TM, Potkin S, Shen L, Buckholtz N, Kaye J, Dolen S, Quinn J, Schneider L, Pawluczyk S, Spann BM, Brewer J, Vanderswag H, Heidebrink JL, Lord JL, Petersen R, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Stern Y, Honig LS, Bell KL, Morris JC, Mintun MA, Schneider S, Marson D, Griffith R, Clark D, Grossman H, Tang C, Marzloff G, deToledo-Morrell L, Shah RC, Duara R, Varon D, Roberts P, Albert MS, Pedroso J, Toroney J, Rusinek H, de Leon MJ, De Santi SM, Doraiswamy P, Petrella JR, Aiello M, Clark CM, Pham C, Nunez J, Smith CD, Given CA 2nd, Hardy P, Lopez OL, Oakley M, Simpson DM, Ismail M, Brand C, Richard J, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Diaz-Arrastia R, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Laubinger MM, Bartzokis G, Silverman DH, Lu PH, Graff-Radford NR, Parfitt F, Johnson H, Farlow M, Herring S, Hake AM, van Dyck CH, MacAvoy MG, Benincasa AL, Chertkow H, Bergman H, Hosein C, Black S, Graham S, Caldwell C, Hsiung GY, Feldman H, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Wu CK, Johnson N, Mesulam M, Sadowsky C, Martinez W, Villena T, Turner S, Johnson KB, Behan KE, Sperling RA, Rentz DM, Johnson KA, Rosen A, Tinklenberg J, Ashford W, Sabbagh M, Connor D, Jacobson S, Killiany R, Norbash A, Nair A, Obisesan TO, Jayam-Trouth A, Wang P, Lerner A, Hudson L, Ogrocki P, DeCarli C, Fletcher E, Carmichael O, Kittur S, Mirje S, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Hendin BA, Scharre DW, Kataki M, Zimmerman EA, Celmins D, Brown AD, Gandy S, Marenberg ME, Rovner BW, Pearlson G, Blank K, Anderson K, Saykin AJ, Santulli RB, Englert J, Williamson JD, Sink KM, Watkins F, Ott BR, Wu CK, Cohen R, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J.

Author information

  • 1Division of Epidemiology, University of California, Berkeley, USA. rlo@berkeley.edu

Abstract

OBJECTIVE:

To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).

METHODS:

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005-2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.

RESULTS:

CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ(42). Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.

CONCLUSION:

The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD.

Comment in

PMID:
22491869
[PubMed - indexed for MEDLINE]
PMCID:
PMC3345787
Free PMC Article
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